Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious keyâvalue store encoding (OKVS) mechanism is introduced to enable secure and efficient keyâvalue mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
AI systems rely heavily on high-quality training data, yet provenance tracking remains fragmented and vulnerable to manipulation. This study presents a blockchain-enabled data provenance framework designed to bring transparency and verifiability to AI training pipelines. The architecture records dataset lineage, preprocessing steps, annotation events and model updates using immutable smart contract transactions. A lightweight off-chain storage mechanism reduces blockchain overhead while maintaining audit guarantees. The system was tested with three machine learning pipelines involving image classification, text processing and sensor analytics. Results show a 95 percent reduction in provenance disputes and full traceability across all data contributors. Smart contracts automate compliance checks and access permissions, ensuring that only validated datasets feed into the training process. The framework improves accountability for AI ethics, model bias evaluation and regulatory reporting. Experiments confirm that blockchain latency does not significantly affect pipeline throughput due to parallelized validation nodes. The work demonstrates how decentralized technologies can support trustworthy AI development. Future research will explore integration with zero-knowledge proofs to further enhance confidentiality.
The proliferation of Internet of Things (IoT) applications and on-demand logistics has fostered crowdsourced delivery systems where dynamic coordination among senders, couriers, and receivers enables efficient lastmile logistics. However, centralized dispatching exposes privacy and reliability risks, including single points of failure and leakage of routing and transaction data. Blockchainbased Payment Channel Networks (PCNs) address these limitations by moving frequent interactions off-chain while maintaining verifiable settlement on-chain. This paper presents a blockchain-anchored privacy-preserving path optimization protocol that supports variable-amount multihop payments over PCNs. By combining Pedersen commitments and lightweight zero-knowledge proofs (ZKPs), the protocol verifies transaction correctness without revealing amounts, and employs blind-channel operations to prevent intermediaries from accessing sensitive data. Experimental results show that the proposed scheme achieves strong privacy protection and scalability with low computational cost, making it suitable for blockchain-based IoT delivery environments.
ExecMesh introduces cryptographically verifiable computation as a foundational primitive for regulatory compliance and audit trail requirements in AI/ML systems [1â3]. By combining commitmentbased verification with secure multi-party oracles and a two-tier regulatory architecture, ExecMesh enables enterprises to meet FDA, SEC, and EU AI Act requirements while maintaining the benefits of decentralized infrastructure. Immediate Value Proposition: ExecMesh provides immediate value as an audit trail and provenance layer for regulated AI systems, independent of advances in zero-knowledge proof technology. Even without full verification of large neural networks, the system delivers cryptographic guarantees for data integrity, execution timestamps, and pipeline reproducibilityâmeeting core regulatory requirements today.
This publication introduces Zero-Knowledge Behavioral Proof (ZKBP) as a post-biometric authentication primitive designed for the QADMON canonical security framework. ZKBP replaces traditional biometric and password-based identity with cryptographically verifiable behavioral continuity. The protocol proves liveness, integrity and continuity of behavior without revealing biometric templates, raw behavioral signals, or any permanent human identifier. The package includes: - Formal cryptographic definition of ZKBP - Security proofs under LWE-based post-quantum assumptions - Comprehensive threat model (AI imitation, replay, side-channels, insider threats) - Protocol specification in JSON - Comparative security tables (CSV) - Multilingual human-readable documentation (EN, RU, HE, ZH, AR) - Implementation notes for PQC + TEE environments This module follows the canonical QADMON axiom: FSIG â Cryptographic Key FSIG = Zero-Knowledge Behavioral Proof The only cryptographic secret is a post-quantum key stored inside a Trusted Execution Environment (TEE). This work is published as Module 02 of the QADMON Canonical Security Framework.
The exponential growth of sophisticated cyber threats in Internet of Things (IoT) environments has exposed fundamental weaknesses in existing Cyber Threat Intelligence (CTI) platforms, including centralized architectures, trust deficits, privacy vulnerabilities, and single points of failure. To overcome these limitations, this paper proposes BlockIntelChain, a blockchain-based framework for secure, scalable, and collaborative CTI sharing across distributed IoT networks. The system integrates a hybrid consensus mechanism that combines Proof-of-Stake with reputation-based validator selection, supported by a multi-layered privacy framework employing Differential Privacy (DP), Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Secure Multi-Party Computation. BlockIntelChain further embeds Federated Learning (FL) to enable distributed model training directly on IoT edge nodes without exposing raw threat telemetry. Comprehensive evaluations on real-world Malware Information Sharing Platform (MISP) datasets show that BlockIntelChain achieves 923 Transactions per Second at 500 nodes with 99.6% consensus success, while maintaining resilience against 51% and Byzantine attacks tolerating up to 33% malicious validators. Privacy analysis confirms an optimized utility-privacy trade-off, with DP (Δ = 0.1) preserving 92% data utility and ZKP achieving 94% verification accuracy. The FL-based models outperform centralized baselines, reaching 96.4% accuracy for IoT malware classification, 94.7% for phishing detection, and 95.2% for network anomaly identification. Economic modeling validates sustainability through contributor growth (156 â 1,245 in 12 months) and improved contribution quality (0.73 â 0.92). The proposed framework directly benefits Security Operation Centers and edge-deployed IoT systems by enabling real-time threat intelligence exchange with strong security, privacy, and efficiency. Comparative benchmarking demonstrates BlockIntelChain's superiority over MISP, ThreatConnect, and IBM X-Force in decentralization, privacy, and cost efficiency, positioning it as a transformative solution for next-generation privacy-aware CTI ecosystems.
Brian R. Cook, Nicholas Harrigan, Van Touch, Kirt Hainzer · 7 authors
ABSTRACT The arts are envisioned as able to help address the longstanding âimplementation gapâ between research and realisation of the Sustainable Development Goals (SDGs). For SDG2 (Zero Hunger), Forum Theatre offers a participatory alternative to topâdown interventions, yet its impacts have not been evaluated using rigorous, mixedâmethods that are attentive to both quantitative and qualitative data, to spillover effects, or to diffusion over time. This study analyses 13 performances in Northwest Cambodia, each attended by 50â150 people, with followâup interviews conducted with 66 attendees 1 year later. Results identify a replicable impact pathway: learning correlates with onâfarm behaviour change, which is predictive of knowledgeâsharing with nonâattendees, whereas recollection alone does not. By evidencing this process, the analysis provides rare empirical proof of theatre's effectiveness as a catalyst for change. More broadly, it evidences a replicable pathway for achieving the SDGs, but one that requires moving beyond informationâtransfer models toward participatory interventions that foster dialogue, critical reflection, forum, and the collective diffusion of new practices. A short documentary and accompanying video of performances are available to illustrate the process and support others seeking to replicate or adapt the approach in different contexts.
Reverse Mathematics is a program in mathematical logic that investigates the minimal axiomatic subsystems of second-order arithmetic required to prove theorems of ordinary mathematics. Developed primarily by Harvey Friedman and Stephen Simpson, this field seeks to "go backwards" from established mathematical theorems to determine the precise set-existence principles necessary for their proofs. The central framework for this analysis is second-order arithmetic ($Z_2$), which formalizes natural numbers and sets of natural numbers. By working within weak base theories, typically Recursive Comprehension Axiom Zero (RCA$_0$), researchers classify a vast array of mathematical theorems into a hierarchy of five main subsystems: RCA$_0$, Weak König's Lemma (WKL$_0$), Arithmetical Comprehension Axiom Zero (ACA$_0$), Arithmetical Transfinite Recursion Zero (ATR$_0$), and $Pi^1_1$-Comprehension Axiom Zero ($Pi^1_1$-CA$_0$). This paper provides a comprehensive overview of Reverse Mathematics, detailing its historical development, core methodology, the characteristics of the "Big Five" subsystems, and representative mathematical theorems classified within each. It explores the philosophical implications of this program, highlighting how it unveils the precise logical and foundational microstructure underlying seemingly diverse mathematical results, thereby contributing to a deeper understanding of the inherent strengths and dependencies of mathematical knowledge.
The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (â$85 at 20 gwei), document submission around 85 thousand gas (â$6), and revocation about 50 thousand gas (â$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10â30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The artificial intelligence (AI) infrastructures have been centralized leading to limited accessibility, monopoly of computational resources, and an uneven distribution of services. CloudChain is a decentralized AI compute market that is made out of blockchain and can solve these challenges with a transparent, trustless, and fair system. It brings together decentralized storage, smart contracts, as well as token incentives to allow fairness, privacy, and auditing. Privacy is ensured through encryption and zero-knowledge proofs, task allocation, distribution of rewards and enforcement of SLA is automated through smart contracts. The performance metrics measured in a 30-day simulation of the major cloud providers (AWS, Google Cloud, Azure, Hetzner, Lambda Labs) and the community nodes included the performance measures of latency, throughput, and SLA compliance, as well as token allocation and resource utilization. Findings indicate that CloudChain does provide the necessary balance in the workloads, high quality in the service delivery, and equitable rewards among heterogeneous members. The suggested framework envisioned will create a democratized, secure, and sustainable platform of decentralized AI, enabling innovation, openness, and diversity of global AI ecosystems.
Prescriptive analytics seeks to identify optimal interventions for achieving desired outcomes, with causal inference playing a pivotal role in assessing intervention impacts on complex systems. However, existing approaches frequently neglect critical data privacy considerations and provide no means to verify the integrity of their recommendations. These limitations hinder its adoption in high-stakes domains such as healthcare and finance. In this paper, we introduce, zkCLEAR, a zero-knowledge proof (ZKP)-based C ausal Inference ( LEA rning and R easoning) framework for privacy-preserving and verifiable prescriptive analytics. Our solution allows data owners or service providers to cryptographically prove the validity of prescriptive conclusions derived from causal analysis without disclosing sensitive source data or proprietary causal models. We develop a suite of ZKP-friendly causal operators to build efficient causal modules, including structure learning, parameter learning, probabilistic inference, and counterfactual reasoning. To optimize performance, we also introduce a workflow decomposition strategy to facilitate efficient proof generation for complex workloads. We demonstrate the utility of zkCLEAR through three real-world applications. The framework faithfully follows the behavior of non-ZKP counterparts, with moderate overheads for privacy and verifiability. Additionally, we evaluate its efficiency and scalability using real-world datasets. It shows up to a 35.1Ă speedup in proof generation time and a 214.5Ă reduction in proof size compared to current general-purpose ZKP systems.
This paper addresses the critical need for accountability in artificial intelligence (AI) systems, particularly in domains where decisions have significant societal and ethical implications. We propose a novel framework leveraging auditable attestations to ensure provable compliance with predefined standards and regulations. The core of our approach involves generating verifiable proofs about the behavior and characteristics of machine learning models, allowing for independent audits and assessments. We explore the theoretical foundations of such attestations, focusing on cryptographic techniques like zero-knowledge proofs and secure multi-party computation, which enable the verification of model properties without revealing sensitive information. Furthermore, we discuss the practical implementation of our framework, including the design of attestation protocols, the selection of relevant model properties to verify, and the development of tools for generating and validating attestations. We illustrate the effectiveness of our approach through case studies in areas such as fairness in lending, transparency in healthcare, and safety in autonomous driving. Our results demonstrate the potential of auditable attestations to enhance trust and accountability in AI systems, fostering responsible innovation and deployment.
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise â proof â encrypt â aggregate) is proven to be necessaryâno efficient alternative existsâand universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise â proof â encrypt â aggregate) is proven to be necessaryâno efficient alternative existsâand universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
Thus, the synergy of artificial intelligence (AI)-based technologies and digital financial transactions require secure anonymized methods while retaining the effectiveness of AI-based fraud-detection. This systematic review investigates stateof-the-art means of enhancing privacy assurance in ML by leveraging innovative schemes to safeguard money transfers in electronic platforms. Many privacy-preserving techniques are available and can be adopted by financial institutions to analyses encrypted data these include homomorphic encryption and federated learning. Employing these methods, AI models can identify fraudulent behavior patterns while at the same time not compromising on the privacy of single transactions. There is an extra level of security or anonymity given x by zero-knowledge proof which allows for the verification of the transactions without disclosing the data behind such transactions. Differential privacy is also used to apply noise on data to ensure that no distinguishing data set is used by the algorithm while ensuring the data is useful for statistical purposes for the ML models used. As much as its integration offers potential in carrying these privacy-shields presents some considerations. Mainly, they improve security and usersâ confidence but at the same time introduce computation cost and system intricacy. This review therefore looks at different implementation strategies and hybrid solutions which employ several ideas aimed at maintaining high efficiency of the applied privacy-preserving techniques. Security: Advanced developments in hardware acceleration and algorithms have brought into use these methods nearer to real life applications. It also explores areas of future development including quantum protection of privacy and privacy preserving AI systems. Nonetheless, time and again there are instances where researchers experienced difficulties in the actual implementation such as the approaches may not be scalable, in other words may not well work for large data sets, or that there is need to standardize these models for privacy-preserving AI to be well embraced as it remains one of the most important revolutions by which the safety of financial systems in the digital world can be enhanced. As trading volumes increase and the regulation of how clientsâ data is used gets stricter, these technologies will be at the heart of shielding consumer information whilst facilitating enhanced fight against fraud.
The accelerating digitalization of critical national infrastructures has underscored the urgent need for sovereign control over data, trust, and governance in cyberspace. Traditional centralized systems, while functional, are increasingly vulnerable to single points of failure, unauthorized access, and opaque accountability structures. Against this backdrop, blockchain technologies offer decentralized trust, immutable record-keeping, and programmable compliance mechanisms that can be embedded into national data infrastructures to reinforce digital sovereignty. This paper investigates how blockchain-backed architectures can serve as foundational enablers of sovereign control over data flows, policy enforcement, and audit transparency within a nation-state context. Using a multi-layered methodology that combines policyâtechnology mapping, comparative analysis of governance frameworks, and case studies across e-government, healthcare, and energy utilities, the study introduces a sovereignty-by-design blockchain framework tailored for Malaysia and ASEAN member states. Results demonstrate that blockchain-based infrastructures improve auditability by over 40%, reduce compliance latency by 35%, and enhance cross-border contractual assurance through integration with ASEAN Model Contractual Clauses (MCCs). The study also highlights the role of privacy-enhancing technologies (PETs) such as confidential computing and zero-knowledge proofs in aligning blockchain with personal data protection laws.
In a rapidly digitalizing world, identity verification has become the cornerstone of secure online interaction. Traditional authentication models, which depend on centralized authorities and password-based systems, are increasingly vulnerable to breaches, identity theft, and data manipulation. Blockchain-backed identity systems offer a promising alternative by decentralizing trust, ensuring immutability, and empowering users with self-sovereign control over their credentials. This review explores how blockchain technology enhances authentication reliability through decentralization, cryptographic assurance, and automation. The paper first examines the fundamentals of blockchain-based identity management, including decentralized identifiers (DIDs), verifiable credentials (VCs), and smart contracts that automate credential verification and revocation. It then presents the architectural components of blockchain identity systems, highlighting how cryptographic hashing, distributed consensus, and off-chain storage combine to create secure yet compliant authentication workflows. The analysis demonstrates that blockchain-backed identity frameworks significantly improve authentication reliability by removing single points of failure, enhancing data integrity, and enabling privacy-preserving verification through mechanisms like zero-knowledge proofs. Comparative evaluation with traditional systems reveals that blockchain ensures superior resilience, transparency, and user control, albeit with challenges in scalability, interoperability, and key management.